Process Control in Semiconductors March 28, 2006.

Process Control in Semiconductors

March 28, 2006

March 28, 2006 Process Control in Semiconductors

2

Agenda

• What is process control

• Why is control needed

• Types of control used

• Importance of control from a business standpoint

• How a controller is developed

• Questions


3

Why do we need Process Control

• Moore’s Law states that transistors on a chip will double roughly every two years

• In order to keep up, features on a chip need to decrease in size (e.i. 120 nm, 90 nm, 60nm etc.)

• The smaller the features the more critical control is towards yields


4

Types of Control Used

• Run-to-Run Control (RtR): automatic change in the process recipe for a given run based on feed-back data from post-process metrology and feed-forward data from previous operations.


5

Run-to-Run Control (RtR)

• Variability is shifted from the controlled (output) variables to the manipulated (input) variables

• Not all variability can be controlled within a given process

R tR C ontro lM odel

P rocessingToo l

M etro logyToo l

P rocess S etP o in ts

FeedbackM etro logy D ata

Feed ForwardM etro logy D ata

Output Variables

Variability


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Run-to-Run Control (RtR)Transferring Variability

Input Output

PolishTime

(s)

Film Thickness

(A)

100 3550

100 4410

100 3283

100 3710

100 3425

StDev 0 440

Input Output

PolishTime

(s)

FilmThickness

(A)

105 3460

110 3530

90 3575

103 3495

98 3571

StDev 7.6 49

Without RtR With RtR


7

Run-to-Run Control (RtR)Transferring Variability

Input Output

PolishTime

(s)

Film Thickness

(A)

100 3550

100 4410

100 3283

100 3710

100 3425

StDev 0 440

Input Output

PolishTime

(s)

FilmThickness

(A)

105 3460

110 3530

90 3575

103 3495

98 3571

StDev 7.6 49

Without RtR With RtR

Note how the variability is transferred from the film thickness to the input polish time.

The deviation in the output (which relates to product quality) is reduced ~10x, while the deviation in the input (which does not relate to product quality) is increased.


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Importance of Control to the Company

• Economic Importance– Increase yield– Decrease cycle time

Reducing re-workDecreasing bottlenecks

• Competitive Value (Business Strategies)– Use as trading chips


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Significance of Increased Yields

Theoretical Example

• Assume 1000 wafers/week are processed

• Assume 250 good die/wafer

• Total of 250,000 die per week


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Significance of Increased Yields (cont.)

Theoretical Example

• If you achieve a 5% yield increase, now you have 262 good die/wafer

• Total of 262,000 good die/week

• Increase of 12,000 good die/week

• Assume avg. selling price is $100/die

• Revenue increases by $1.2M/week or $60M/year


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Significance of Cycle Time

Theoretical Example

• Assume 1000 wafers/week are processed

• Assume 250 good die/wafer

• Total of 250,000 good die per week


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Significance of Cycle Time (Cont.)

Theoretical Example

• If you achieve a 5% greater throughput due to less cycle time, now you process 1050 wafer/week.

• Total of 262,500 good die/week

• Increase of 12,500 good die/week

• Assume avg. selling price is $100

• Revenue increases by $1.25M/week or $62.5 M/year


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Business Strategies

• Trade control IP for transistor knowledge

• Trade control IP for reduced wafer price (from foundry)


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Deciding when a new control is needed

• Proactive approach (R&D)– Anticipated future needs

Develop solutions necessary for future products

• Reactive approach– Quality issues (i.e. bad yield)– Productivity issues

Bottleneck toolsPoor processing (i.e. re-works)


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Developing a new controller

• Identify the process to be controlled – Identify the target outputs– Identify the inputs necessary to control

• Develop the algorithm– The control model– The control law– EWMA for state estimation

• Test the controller in a development environment

• Roll out the controller in the FAB in passive mode

• Roll out the controller in the FAB in production mode


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The Control Model

• The model explains the relationship between the inputs and the output

• In APC, this is almost always a linear model of the form

– y the output (measurement)– u the input (control knob)– c the intercept (output when the input is zero)– b gain (affect of changing the input on the output)

cbuy


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The Control Law

• Once we have the process model, we can calculate the inputs required to achieve the output target– Let us designate the output target as “T”– Starting with the model

– Replace y with T and solve for u

cbuy

b

cTu


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Determining the “State”

• Again, starting with the model

• Historical data is used to estimate the parameters b or c– Option 1: set c as a constant and estimate b– Option 2: set b as a constant and estimate c

• Estimating the parameters from historical data is the main focus of APC

cbuy


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The EWMA Filter

• Since things can change over time, newer data is more relevant when estimating the unknown model parameter

• Some examples are:– CMP pad wear– Tools get dirty (etch deposition, stepper lenses, etc.)– Upstream process changes (layer thickness, material

properties, etc.)

• Measurements are usually discounted exponentially


20

1.00

0.70

0.49

0.340.24

0.17 0.12 0.08 0.06 0.04 0.030

0.2

0.4

0.6

0.8

1

0 1 2 3 4 5 6 7 8 9 10

Run (i )

Weig

ht

Facto

r (w)

The EWMA Filter

• Measurements are weighted by an exponentially decreasing factor, ωi = (1-λ)i

3.0

n

nknkkk xxxxx

wwwwwwww

210

22110)EWMA(


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Choosing the EWMA filter constant, λ

• When λ is close to 1.0– Only the most recent measurement is considered– Results in fast responses to process changes– Makes you more sensitive to noise

• Whenλ is close to 0.0– All measurements are weighted equally (measurements are

averaged)– Less sensitivity to noise– Results in slow responses to process changes

• Typically, λ is chosen to be between 0.2 and 0.5

• The most common choice is λ = 0.3


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Choosing the EWMA Tuning Parameter, λ

=0.1

-3

-2

-1

0

1

2

3

4

0 10 20 30 40 50 60 70 80 90

Run Number

Sta

te StateEWMA

=0.9

-3

-2

-1

0

1

2

3

4

0 10 20 30 40 50 60 70 80 90

Run Number

Sta

te StateEWMA

• Less sensitive to noise• Slower response to step

disturbances

• More sensitive to noise• Quicker response to step

disturbances


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EWMA Updating Equation

• If we have enough data, the weight on the oldest measurements is negligible

• In this case, the EWMA estimate is a weighted average of the new observation and the old estimate

ktt xxx 1ˆ)1(ˆ


24

Deposition Example

Oxide

Oxide Thickness


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Deposition (cont.)

• Input– Deposition Time

• Output– Oxide Thickness


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Deposition (cont.)

• Control model:

– y is the Film Thickness– b is the Dep rate– u is the Dep time– c is the intercept

• Assume that the intercept is constant

• Estimate the dep rate, b, from historical data

cbuy


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Deposition (cont.)

• Control Law

– Starting with the model:

– Solve for Dep Time:

Dep thickness = Dep rate * Dep time + intercept

rate Dep

intercept - thicknessDep timeDep


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Deposition (cont.)

• Use an EWMA filter with λ = .4 to find EWMA(Dep rate)

• If the target is T=100, what should the input be for the next run?

Historical Data

Run Thickness Dep Time Intercept Dep Rate

(nm) (s) (nm/s)

1 101 15.1 0 6.69

2 102 15.4 0 6.62

3 99 14.9 0 6.64

4 97 14.8 0 6.55

5 98 15 0 6.53


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Deposition (cont.)

• Recall:

• Therefore:

• EWMA(dep rate) = 6.64 nm/s

n

nknkkk xxxxx

wwwwwwww

210

22110)EWMA(

1296.216.36.6.1

1296.*53.6216.*55.636.*64.66.*62.61*69.6

EWMA(dep rate) =


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Deposition (cont.)

• Recall:

• Using EWMA(Dep rate):

• Dep Time = 15.1 s

rate Dep

intercept - thicknessDep timeDep

62.6

0100 timeDep


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Conclusion

• Controlling certain processes within nanometers is important to the yield of the product

• Having tight control effects the economic impact to the company

• EWMA is a way to estimate a parameter from historical data


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Questions??


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Trademark Attribution

AMD, the AMD Arrow logo and combinations thereof are trademarks of Advanced Micro Devices, Inc. in the United States and/or other jurisdictions. Other names used in this presentation are for identification purposes only and may be trademarks of their respective owners.

©2005 Advanced Micro Devices, Inc. All rights reserved.

Process Control in Semiconductors March 28, 2006.

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